Class/Object

ksb.csle.didentification.verification.check

CategoryTClosenessCheck

Related Docs: object CategoryTClosenessCheck | package check

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class CategoryTClosenessCheck extends AnyRef

This class is a base class to check whether the anonymized data satisfies the T-closeness constraints.

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Instance Constructors

  1. new CategoryTClosenessCheck()

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Value Members

  1. final def !=(arg0: Any): Boolean

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  2. final def ##(): Int

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  3. final def ==(arg0: Any): Boolean

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  4. final def asInstanceOf[T0]: T0

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  5. def clone(): AnyRef

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  6. final def eq(arg0: AnyRef): Boolean

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  7. def equals(arg0: Any): Boolean

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  8. def finalize(): Unit

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  9. final def getClass(): Class[_]

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  10. def getOverallTClosenessTable(src: DataFrame, sens: Array[String]): Map[List[Any], Long]

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  11. def getOverallTClosenessTable(src: DataFrame, sens: String): Map[List[Any], Long]

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  12. def getTClosenessValue(eqTable: Map[List[Any], Long], allTable: Map[List[Any], Long]): Double

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  13. def getTClosenessValue(src: DataFrame, columnNames: Array[String], sens: Array[String]): Double

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    Checks the anonymized dataframe satisfies the given l-Diversity constraint.

    Checks the anonymized dataframe satisfies the given l-Diversity constraint.

    src

    The anonymized dataframe

    returns

    Boolean return true if satisfying the given l-diversity constraint

  14. def hashCode(): Int

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  15. final def isInstanceOf[T0]: Boolean

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  16. final def ne(arg0: AnyRef): Boolean

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  17. final def notify(): Unit

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  18. final def notifyAll(): Unit

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  19. final def synchronized[T0](arg0: ⇒ T0): T0

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  20. def tClosenessCheck(src: DataFrame, columnNames: Array[String], sens: Array[String], tClosenessValue: Double): Boolean

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    The common L-diversity method just checks whether the number of sensitive attributes is greater than given L-constraints.

    The common L-diversity method just checks whether the number of sensitive attributes is greater than given L-constraints. Generally, an equivalence class is l-diverse if contains at least 'l' well-represented values for the sensitive attribute. A table is l-diverse if every equivalence is l-diverse

    src

    The anonymized dataframe

    returns

    Double return the l-diversity value

  21. def toString(): String

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  22. final def wait(): Unit

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  23. final def wait(arg0: Long, arg1: Int): Unit

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  24. final def wait(arg0: Long): Unit

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